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A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion

Fig 7

Applying hMFC to an empirical dataset.

A) The estimated posterior means for each subject of a_i and when fitting hMFC to an empirical dataset of Shekhar and Rahnev (2021) [58]. Based on these empirical fits of the per-subject parameters we simulated a new dataset and refitted two variants of hMFC where a_i was freely estimated or fixed to .9995 (following Gupta and Brody (2022) [21], Roy et al. (2021) [23]). B) When a_i is freely estimated we see a good recovery for the parameters a_i and , and the criterion trajectory (example subject with first 500 trials shown). C) With a_i fixed to .9995 we see a systematic underestimation of . Most importantly, this also affects the recoverability of the criterion trajectory. D) For all 20 subjects the correlation between the true and estimated criterion trajectory is higher with a_i as a free parameter compared to having it fixed. Similarly, the root mean squared error (RMSE) is lower when a_i is estimated. However, accurate recovery of a_i at low trial counts requires a hierarchical estimation procedure (S1 Fig).

doi: https://doi.org/10.1371/journal.pcbi.1013291.g007